In recent times, feature extraction was the focus of many researches due to its usefulness in the machine learning and pattern recognition fields. Feature extraction mainly aims to extract informative representations from the original set of features. This can be carried out using various ways. The proposed method is targeting a hybrid linear feature extraction scheme for supervised multi-class classification problems. Inspired by recent robust sparse LDA and Inter-class sparsity frameworks, we will propose a unifying criterion that is able to retain these two powerful linear discriminant method’s advantages. Thus, the obtained transformation encapsulates two different types of discrimination, the inter-class sparsity and robust Linear Discriminant Analysis with feature selection. We will introduce an iterative alternating minimization scheme in order to estimate the linear transform and the orthogonal matrix. The linear transform is efficiently updated via the steepest descent gradient technique. We will also introduce two initialization schemes for the linear transform. The proposed framework is generic in the sense that it allows the combination and tuning of other linear discriminant embedding methods. According to the experiments which have been carried out on several datasets including faces, objects and digits, the proposed method was able to outperform the competing methods in most cases.

近年来，特征提取由于其在机器学习和模式识别领域中的有用性而成为许多研究的焦点。特征提取主要旨在从原始特征集中提取信息性表示。这可以使用各种方式来执行。提出的方法针对针对监督的多类分类问题的混合线性特征提取方案。受近期健壮的稀疏LDA和类间稀疏性框架的启发，我们将提出一个统一的标准，该标准能够保留这两种强大的线性判别方法的优势。因此，所获得的变换封装了两种不同类型的判别：类间稀疏性和具有特征选择的鲁棒线性判别分析。我们将介绍一个迭代交替最小化方案，以估计线性变换和正交矩阵。线性变换通过最速下降梯度技术得到有效更新。我们还将介绍两种用于线性变换的初始化方案。在允许组合和调整其他线性判别嵌入方法的意义上，提出的框架是通用的。根据已经在包括面部，物体和数字的几个数据集上进行的实验，在大多数情况下，所提出的方法能够胜过竞争方法。在允许组合和调整其他线性判别嵌入方法的意义上，提出的框架是通用的。根据已经在包括面部，物体和数字的几个数据集上进行的实验，在大多数情况下，所提出的方法能够胜过竞争方法。在允许组合和调整其他线性判别嵌入方法的意义上，提出的框架是通用的。根据已经在包括面部，物体和数字的几个数据集上进行的实验，在大多数情况下，所提出的方法能够胜过竞争方法。